Dados Geoespaciais¶

Instalando o Ambiente¶

In [1]:

!pip install geopandas shapely mapclassify libpysal splot esda pysal contextily
!pip install --upgrade pyshp
!pip install --upgrade descartes
!pip install --upgrade fiona

!pip install geopandas shapely mapclassify libpysal splot esda pysal contextily !pip install --upgrade pyshp !pip install --upgrade descartes !pip install --upgrade fiona

Requirement already satisfied: geopandas in d:\gitrepos\curso_python_gis\.venv\lib\site-packages (0.14.3)

[notice] A new release of pip is available: 24.0 -> 25.0.1
[notice] To update, run: python.exe -m pip install --upgrade pip

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[notice] A new release of pip is available: 24.0 -> 25.0.1
[notice] To update, run: python.exe -m pip install --upgrade pip

Collecting descartes
  Downloading descartes-1.1.0-py3-none-any.whl.metadata (2.4 kB)
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Downloading descartes-1.1.0-py3-none-any.whl (5.8 kB)
Installing collected packages: descartes
Successfully installed descartes-1.1.0

[notice] A new release of pip is available: 24.0 -> 25.0.1
[notice] To update, run: python.exe -m pip install --upgrade pip

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Installing collected packages: fiona
  Attempting uninstall: fiona
    Found existing installation: fiona 1.9.6
    Uninstalling fiona-1.9.6:
      Successfully uninstalled fiona-1.9.6
Successfully installed fiona-1.10.1

[notice] A new release of pip is available: 24.0 -> 25.0.1
[notice] To update, run: python.exe -m pip install --upgrade pip

Sobre os dados¶

Baixados em https://www.ibge.gov.br/estatisticas/downloads-estatisticas.html

Dicionário de dados para Agregados preliminares por setores censitários:

https://view.officeapps.live.com/op/view.aspx?src=https%3A%2F%2Fftp.ibge.gov.br%2FCensos%2FCenso_Demografico_2022%2FAgregados_por_Setores_Censitarios_preliminares%2Farquivos_complementares%2FDicionario_de_dados_agregados_preliminares.xlsx&wdOrigin=BROWSELINK

Importando pacotes¶

In [2]:

import pandas as pd
import numpy as np
import zipfile

# para gráficos
import matplotlib.pyplot as plt
import seaborn as sns

# para a análise de dados espaciais
import geopandas as gp

import mapclassify as mc
import contextily as cx

import pandas as pd import numpy as np import zipfile # para gráficos import matplotlib.pyplot as plt import seaborn as sns # para a análise de dados espaciais import geopandas as gp import mapclassify as mc import contextily as cx

Baixando Arquivos¶

In [3]:

!wget https://255ribeiro.github.io/curso_python_gis/geo_py/BA_Malha_Preliminar_2022.zip

!wget https://255ribeiro.github.io/curso_python_gis/geo_py/BA_Malha_Preliminar_2022.zip

--2024-03-27 13:35:54--  https://255ribeiro.github.io/curso_python_gis/geo_py/BA_Malha_Preliminar_2022.zip
Resolving 255ribeiro.github.io (255ribeiro.github.io)... 185.199.108.153, 185.199.109.153, 185.199.110.153, ...
Connecting to 255ribeiro.github.io (255ribeiro.github.io)|185.199.108.153|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 62263048 (59M) [application/zip]
Saving to: ‘BA_Malha_Preliminar_2022.zip’

BA_Malha_Preliminar 100%[===================>]  59.38M  86.3MB/s    in 0.7s    

2024-03-27 13:35:54 (86.3 MB/s) - ‘BA_Malha_Preliminar_2022.zip’ saved [62263048/62263048]

Extraindo do arquivo zip¶

In [4]:

with zipfile.ZipFile("BA_Malha_Preliminar_2022.zip", 'r') as zip_ref:
  zip_ref.extractall("./BA_Malha_Preliminar_2022")

with zipfile.ZipFile("BA_Malha_Preliminar_2022.zip", 'r') as zip_ref: zip_ref.extractall("./BA_Malha_Preliminar_2022")

Carregando GeoJson¶

In [5]:

df_geo = gp.read_file("./BA_Malha_Preliminar_2022/BA_Malha_Preliminar_2022.json")

df_geo = gp.read_file("./BA_Malha_Preliminar_2022/BA_Malha_Preliminar_2022.json")

Informações do arquivo¶

In [6]:

df_geo.shape

df_geo.shape

Out[6]:

(30642, 30)

In [7]:

df_geo.info()

df_geo.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 30642 entries, 0 to 30641
Data columns (total 30 columns):
 #   Column      Non-Null Count  Dtype   
---  ------      --------------  -----   
 0   CD_SETOR    30642 non-null  object  
 1   AREA_KM2    30642 non-null  float64 
 2   CD_REGIAO   30642 non-null  object  
 3   NM_REGIAO   30642 non-null  object  
 4   CD_UF       30642 non-null  object  
 5   NM_UF       30642 non-null  object  
 6   CD_MUN      30642 non-null  object  
 7   NM_MUN      30642 non-null  object  
 8   CD_DIST     30642 non-null  object  
 9   NM_DIST     30642 non-null  object  
 10  CD_SUBDIST  30642 non-null  object  
 11  NM_SUBDIST  5491 non-null   object  
 12  CD_MICRO    30642 non-null  object  
 13  NM_MICRO    30642 non-null  object  
 14  CD_MESO     30642 non-null  object  
 15  NM_MESO     30642 non-null  object  
 16  CD_RGI      30642 non-null  object  
 17  NM_RGI      30642 non-null  object  
 18  CD_RGINT    30642 non-null  object  
 19  NM_RGINT    30642 non-null  object  
 20  CD_CONCURB  11112 non-null  object  
 21  NM_CONCURB  11112 non-null  object  
 22  v0001       30642 non-null  object  
 23  v0002       30642 non-null  object  
 24  v0003       30642 non-null  object  
 25  v0004       30642 non-null  object  
 26  v0005       30642 non-null  object  
 27  v0006       30642 non-null  object  
 28  v0007       30642 non-null  object  
 29  geometry    30642 non-null  geometry
dtypes: float64(1), geometry(1), object(28)
memory usage: 7.0+ MB

In [8]:

df_geo.describe()

df_geo.describe()

Out[8]:

	AREA_KM2
count	30642.000000
mean	18.430926
std	54.460159
min	0.001235
25%	0.061144
50%	0.254225
75%	8.604529
max	1484.628804

In [9]:

df_geo.head()

df_geo.head()

Out[9]:

	CD_SETOR	AREA_KM2	CD_REGIAO	NM_REGIAO	CD_UF	NM_UF	CD_MUN	NM_MUN	CD_DIST	NM_DIST	...	CD_CONCURB	NM_CONCURB	v0001	v0002	v0003	v0004	v0005	v0006	v0007	geometry
0	290010805000001P	1.252312	2	Nordeste	29	Bahia	2900108	Abaíra	290010805	Abaíra	...	None	None	807	421	419	2	2.668919	0.000000	296	POLYGON ((-41.65404 -13.25992, -41.65423 -13.2...
1	290010805000002P	0.258089	2	Nordeste	29	Bahia	2900108	Abaíra	290010805	Abaíra	...	None	None	416	234	234	0	2.461538	11.834320	169	POLYGON ((-41.66355 -13.25025, -41.66425 -13.2...
2	290010805000003P	0.636238	2	Nordeste	29	Bahia	2900108	Abaíra	290010805	Abaíra	...	None	None	681	392	392	0	2.522222	5.185185	270	POLYGON ((-41.66645 -13.25044, -41.66702 -13.2...
3	290010805000004P	1.395440	2	Nordeste	29	Bahia	2900108	Abaíra	290010805	Abaíra	...	None	None	626	344	344	0	2.608333	2.083333	240	POLYGON ((-41.66355 -13.25025, -41.66345 -13.2...
4	290010805000005P	1.130747	2	Nordeste	29	Bahia	2900108	Abaíra	290010805	Abaíra	...	None	None	276	159	159	0	2.732673	0.990099	101	POLYGON ((-41.65110 -13.25848, -41.65082 -13.2...

5 rows × 30 columns

In [10]:

pd.set_option('display.max_columns', None)
df_geo.head()

pd.set_option('display.max_columns', None) df_geo.head()

Out[10]:

	CD_SETOR	AREA_KM2	CD_REGIAO	NM_REGIAO	CD_UF	NM_UF	CD_MUN	NM_MUN	CD_DIST	NM_DIST	CD_SUBDIST	NM_SUBDIST	CD_MICRO	NM_MICRO	CD_MESO	NM_MESO	CD_RGI	NM_RGI	CD_RGINT	NM_RGINT	CD_CONCURB	NM_CONCURB	v0001	v0002	v0003	v0004	v0005	v0006	v0007	geometry
0	290010805000001P	1.252312	2	Nordeste	29	Bahia	2900108	Abaíra	290010805	Abaíra	29001080500	None	29023	Seabra	2906	Centro Sul Baiano	290013	Brumado	2904	Vitória da Conquista	None	None	807	421	419	2	2.668919	0.000000	296	POLYGON ((-41.65404 -13.25992, -41.65423 -13.2...
1	290010805000002P	0.258089	2	Nordeste	29	Bahia	2900108	Abaíra	290010805	Abaíra	29001080500	None	29023	Seabra	2906	Centro Sul Baiano	290013	Brumado	2904	Vitória da Conquista	None	None	416	234	234	0	2.461538	11.834320	169	POLYGON ((-41.66355 -13.25025, -41.66425 -13.2...
2	290010805000003P	0.636238	2	Nordeste	29	Bahia	2900108	Abaíra	290010805	Abaíra	29001080500	None	29023	Seabra	2906	Centro Sul Baiano	290013	Brumado	2904	Vitória da Conquista	None	None	681	392	392	0	2.522222	5.185185	270	POLYGON ((-41.66645 -13.25044, -41.66702 -13.2...
3	290010805000004P	1.395440	2	Nordeste	29	Bahia	2900108	Abaíra	290010805	Abaíra	29001080500	None	29023	Seabra	2906	Centro Sul Baiano	290013	Brumado	2904	Vitória da Conquista	None	None	626	344	344	0	2.608333	2.083333	240	POLYGON ((-41.66355 -13.25025, -41.66345 -13.2...
4	290010805000005P	1.130747	2	Nordeste	29	Bahia	2900108	Abaíra	290010805	Abaíra	29001080500	None	29023	Seabra	2906	Centro Sul Baiano	290013	Brumado	2904	Vitória da Conquista	None	None	276	159	159	0	2.732673	0.990099	101	POLYGON ((-41.65110 -13.25848, -41.65082 -13.2...

Plotagem com o geopandas¶

https://geopandas.org/en/stable/docs/user_guide/mapping.html

In [11]:

df_geo.plot()

df_geo.plot()

Out[11]:

<Axes: >

In [12]:

fig, ax = plt.subplots(figsize=(10,10))

ax = df_geo.plot(ax=ax, column = "v0001")

fig.show()

fig, ax = plt.subplots(figsize=(10,10)) ax = df_geo.plot(ax=ax, column = "v0001") fig.show()

In [13]:

df_geo.boundary.plot(figsize=(10,10), edgecolor="black")

df_geo.boundary.plot(figsize=(10,10), edgecolor="black")

Out[13]:

<Axes: >

In [14]:

df_geo.boundary.plot(figsize=(10,10), edgecolor="black", lw=.5)

df_geo.boundary.plot(figsize=(10,10), edgecolor="black", lw=.5)

Out[14]:

<Axes: >

Tratando dados¶

In [15]:

df_geo["v0001"] = df_geo["v0001"].astype(float)
df_geo["v0002"] = df_geo["v0002"].astype(float)
df_geo["v0003"] = df_geo["v0003"].astype(float)
df_geo["v0004"] = df_geo["v0004"].astype(float)
df_geo["v0005"] = df_geo["v0005"].astype(float)
df_geo["v0006"] = df_geo["v0006"].astype(float)
df_geo["v0007"] = df_geo["v0007"].astype(float)
# criado novas colunas
df_geo["desnidade_pop"] =  df_geo["v0001"]/df_geo["AREA_KM2"]

df_geo["v0001"] = df_geo["v0001"].astype(float) df_geo["v0002"] = df_geo["v0002"].astype(float) df_geo["v0003"] = df_geo["v0003"].astype(float) df_geo["v0004"] = df_geo["v0004"].astype(float) df_geo["v0005"] = df_geo["v0005"].astype(float) df_geo["v0006"] = df_geo["v0006"].astype(float) df_geo["v0007"] = df_geo["v0007"].astype(float) # criado novas colunas df_geo["desnidade_pop"] = df_geo["v0001"]/df_geo["AREA_KM2"]

In [16]:

df_geo.info()

df_geo.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 30642 entries, 0 to 30641
Data columns (total 31 columns):
 #   Column         Non-Null Count  Dtype   
---  ------         --------------  -----   
 0   CD_SETOR       30642 non-null  object  
 1   AREA_KM2       30642 non-null  float64 
 2   CD_REGIAO      30642 non-null  object  
 3   NM_REGIAO      30642 non-null  object  
 4   CD_UF          30642 non-null  object  
 5   NM_UF          30642 non-null  object  
 6   CD_MUN         30642 non-null  object  
 7   NM_MUN         30642 non-null  object  
 8   CD_DIST        30642 non-null  object  
 9   NM_DIST        30642 non-null  object  
 10  CD_SUBDIST     30642 non-null  object  
 11  NM_SUBDIST     5491 non-null   object  
 12  CD_MICRO       30642 non-null  object  
 13  NM_MICRO       30642 non-null  object  
 14  CD_MESO        30642 non-null  object  
 15  NM_MESO        30642 non-null  object  
 16  CD_RGI         30642 non-null  object  
 17  NM_RGI         30642 non-null  object  
 18  CD_RGINT       30642 non-null  object  
 19  NM_RGINT       30642 non-null  object  
 20  CD_CONCURB     11112 non-null  object  
 21  NM_CONCURB     11112 non-null  object  
 22  v0001          30642 non-null  float64 
 23  v0002          30642 non-null  float64 
 24  v0003          30642 non-null  float64 
 25  v0004          30642 non-null  float64 
 26  v0005          30642 non-null  float64 
 27  v0006          30642 non-null  float64 
 28  v0007          30642 non-null  float64 
 29  geometry       30642 non-null  geometry
 30  desnidade_pop  30642 non-null  float64 
dtypes: float64(9), geometry(1), object(21)
memory usage: 7.2+ MB

In [17]:

df_geo.describe()

df_geo.describe()

Out[17]:

	AREA_KM2	v0001	v0002	v0003	v0004	v0005	v0006	v0007	desnidade_pop
count	30642.000000	30642.000000	30642.000000	30642.000000	30642.000000	30642.000000	30642.000000	30642.000000	30642.000000
mean	18.430926	461.511194	224.417173	224.164447	0.252725	2.745468	2.032173	166.266497	6241.302646
std	54.460159	289.645276	131.152737	131.035016	1.539796	0.455014	3.254504	102.624350	10243.991791
min	0.001235	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	0.061144	248.000000	133.000000	133.000000	0.000000	2.571429	0.000000	90.000000	24.972272
50%	0.254225	440.000000	216.000000	216.000000	0.000000	2.760000	0.921659	160.000000	1692.239809
75%	8.604529	629.000000	298.000000	298.000000	0.000000	2.956522	3.225806	228.000000	8179.894673
max	1484.628804	4253.000000	2170.000000	2170.000000	102.000000	8.000000	100.000000	1397.000000	171696.149844

In [19]:

fig, ax = plt.subplots(figsize=(10,10))

ax = df_geo.plot(ax=ax, column = "desnidade_pop",
                 scheme="quantiles",

                 legend_kwds={"title": "Habitantes por km²"}
                 )

fig, ax = plt.subplots(figsize=(10,10)) ax = df_geo.plot(ax=ax, column = "desnidade_pop", scheme="quantiles", legend_kwds={"title": "Habitantes por km²"} )

Ajustes de color maps¶

color maps padrão https://matplotlib.org/stable/users/explain/colors/colormaps.html

In [20]:

fig, ax = plt.subplots(figsize=(10,10))

ax = df_geo.plot(ax=ax, column = "desnidade_pop",
                 scheme="quantiles",
                 cmap='viridis',
                 legend=True,
                 legend_kwds={"title": "Habitantes por km²"}
                 )

fig, ax = plt.subplots(figsize=(10,10)) ax = df_geo.plot(ax=ax, column = "desnidade_pop", scheme="quantiles", cmap='viridis', legend=True, legend_kwds={"title": "Habitantes por km²"} )

Cmaps customizados¶

https://matplotlib.org/stable/gallery/color/named_colors.html

In [21]:

from matplotlib import colors as mcolors

c_list = ["darkred","red","orangered", "salmon","mistyrose"]

c_map = mcolors.ListedColormap(c_list)

fig, ax = plt.subplots(figsize=(10,10))

ax = df_geo.plot(ax=ax, column = "desnidade_pop",
                 scheme="quantiles",
                 cmap=c_map,
                 legend=True,
                 legend_kwds={"title": "Habitantes por km²"}
                 )

from matplotlib import colors as mcolors c_list = ["darkred","red","orangered", "salmon","mistyrose"] c_map = mcolors.ListedColormap(c_list) fig, ax = plt.subplots(figsize=(10,10)) ax = df_geo.plot(ax=ax, column = "desnidade_pop", scheme="quantiles", cmap=c_map, legend=True, legend_kwds={"title": "Habitantes por km²"} )

usando seletores de cores da web https://coolors.co/

In [22]:

c_list = ["#271f30","#6c5a49","#c8ad55","#9bc59d","#d0fcb3"]

#c_list.reverse()
# for i in range(len(c_list)):
#   if c_list[i][0]!= "#":
#     c_list[i] = "#" + c_list[i]

print(c_list)

c_map = mcolors.ListedColormap(c_list)


fig, ax = plt.subplots(figsize=(10,10))

ax = df_geo.plot(ax=ax, column = "desnidade_pop",
                 scheme="quantiles",
                 cmap=c_map,
                 legend=True,
                 legend_kwds={"title": "Habitantes por km²"}
                 )

c_list = ["#271f30","#6c5a49","#c8ad55","#9bc59d","#d0fcb3"] #c_list.reverse() # for i in range(len(c_list)): # if c_list[i][0]!= "#": # c_list[i] = "#" + c_list[i] print(c_list) c_map = mcolors.ListedColormap(c_list) fig, ax = plt.subplots(figsize=(10,10)) ax = df_geo.plot(ax=ax, column = "desnidade_pop", scheme="quantiles", cmap=c_map, legend=True, legend_kwds={"title": "Habitantes por km²"} )

['#271f30', '#6c5a49', '#c8ad55', '#9bc59d', '#d0fcb3']

Filtrando mapa por valor de coluna¶

In [23]:

fltr = df_geo["NM_MUN"] == "Salvador"
df_geo_ssa = df_geo[fltr]
df_geo_ssa.shape, df_geo.shape

fltr = df_geo["NM_MUN"] == "Salvador" df_geo_ssa = df_geo[fltr] df_geo_ssa.shape, df_geo.shape

Out[23]:

((4552, 31), (30642, 31))

In [24]:

df_geo_ssa.info()

df_geo_ssa.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
Int64Index: 4552 entries, 20719 to 25270
Data columns (total 31 columns):
 #   Column         Non-Null Count  Dtype   
---  ------         --------------  -----   
 0   CD_SETOR       4552 non-null   object  
 1   AREA_KM2       4552 non-null   float64 
 2   CD_REGIAO      4552 non-null   object  
 3   NM_REGIAO      4552 non-null   object  
 4   CD_UF          4552 non-null   object  
 5   NM_UF          4552 non-null   object  
 6   CD_MUN         4552 non-null   object  
 7   NM_MUN         4552 non-null   object  
 8   CD_DIST        4552 non-null   object  
 9   NM_DIST        4552 non-null   object  
 10  CD_SUBDIST     4552 non-null   object  
 11  NM_SUBDIST     4552 non-null   object  
 12  CD_MICRO       4552 non-null   object  
 13  NM_MICRO       4552 non-null   object  
 14  CD_MESO        4552 non-null   object  
 15  NM_MESO        4552 non-null   object  
 16  CD_RGI         4552 non-null   object  
 17  NM_RGI         4552 non-null   object  
 18  CD_RGINT       4552 non-null   object  
 19  NM_RGINT       4552 non-null   object  
 20  CD_CONCURB     4552 non-null   object  
 21  NM_CONCURB     4552 non-null   object  
 22  v0001          4552 non-null   float64 
 23  v0002          4552 non-null   float64 
 24  v0003          4552 non-null   float64 
 25  v0004          4552 non-null   float64 
 26  v0005          4552 non-null   float64 
 27  v0006          4552 non-null   float64 
 28  v0007          4552 non-null   float64 
 29  geometry       4552 non-null   geometry
 30  desnidade_pop  4552 non-null   float64 
dtypes: float64(9), geometry(1), object(21)
memory usage: 1.1+ MB

In [25]:

df_geo_ssa.plot()

df_geo_ssa.plot()

Out[25]:

<Axes: >

Sistema de coordenadas¶

https://www.earthdatascience.org/courses/earth-analytics/spatial-data-r/intro-to-coordinate-reference-systems/

In [26]:

df_geo_ssa.crs

df_geo_ssa.crs

Out[26]:

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

In [27]:

df_geo_ssa = df_geo_ssa.to_crs("31984")
df_geo_ssa.crs

df_geo_ssa = df_geo_ssa.to_crs("31984") df_geo_ssa.crs

Out[27]:

<Projected CRS: EPSG:31984>
Name: SIRGAS 2000 / UTM zone 24S
Axis Info [cartesian]:
- E[east]: Easting (metre)
- N[north]: Northing (metre)
Area of Use:
- name: Brazil - between 42°W and 36°W, northern and southern hemispheres, onshore and offshore.
- bounds: (-42.0, -26.35, -36.0, 0.74)
Coordinate Operation:
- name: UTM zone 24S
- method: Transverse Mercator
Datum: Sistema de Referencia Geocentrico para las AmericaS 2000
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich

Gravando Geojson¶

In [28]:

with open("SSA_BA_Malha_Preliminar_2022.json" , 'w') as file:
    file.write(df_geo_ssa.to_json())

with open("SSA_BA_Malha_Preliminar_2022.json" , 'w') as file: file.write(df_geo_ssa.to_json())

Abrindo GeoJson¶

In [29]:

df_2 = gp.read_file("SSA_BA_Malha_Preliminar_2022.json")
df_2.info()

df_2 = gp.read_file("SSA_BA_Malha_Preliminar_2022.json") df_2.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 4552 entries, 0 to 4551
Data columns (total 32 columns):
 #   Column         Non-Null Count  Dtype   
---  ------         --------------  -----   
 0   id             4552 non-null   object  
 1   CD_SETOR       4552 non-null   object  
 2   AREA_KM2       4552 non-null   float64 
 3   CD_REGIAO      4552 non-null   object  
 4   NM_REGIAO      4552 non-null   object  
 5   CD_UF          4552 non-null   object  
 6   NM_UF          4552 non-null   object  
 7   CD_MUN         4552 non-null   object  
 8   NM_MUN         4552 non-null   object  
 9   CD_DIST        4552 non-null   object  
 10  NM_DIST        4552 non-null   object  
 11  CD_SUBDIST     4552 non-null   object  
 12  NM_SUBDIST     4552 non-null   object  
 13  CD_MICRO       4552 non-null   object  
 14  NM_MICRO       4552 non-null   object  
 15  CD_MESO        4552 non-null   object  
 16  NM_MESO        4552 non-null   object  
 17  CD_RGI         4552 non-null   object  
 18  NM_RGI         4552 non-null   object  
 19  CD_RGINT       4552 non-null   object  
 20  NM_RGINT       4552 non-null   object  
 21  CD_CONCURB     4552 non-null   object  
 22  NM_CONCURB     4552 non-null   object  
 23  v0001          4552 non-null   float64 
 24  v0002          4552 non-null   float64 
 25  v0003          4552 non-null   float64 
 26  v0004          4552 non-null   float64 
 27  v0005          4552 non-null   float64 
 28  v0006          4552 non-null   float64 
 29  v0007          4552 non-null   float64 
 30  desnidade_pop  4552 non-null   float64 
 31  geometry       4552 non-null   geometry
dtypes: float64(9), geometry(1), object(22)
memory usage: 1.1+ MB

In [30]:

df_2.plot()

df_2.plot()

Out[30]:

<Axes: >

Eliminando feição indesejada¶

Identificando feição indesejada -método 1¶

In [31]:

df_2['coords'] = df_2['geometry'].apply(lambda x: x.representative_point().coords[0])

df_2['coords'] = df_2['geometry'].apply(lambda x: x.representative_point().coords[0])

In [32]:

df_2['coords']

df_2['coords']

Out[32]:

0       (555517.6892997203, 8561162.794243902)
1        (555869.385196075, 8561221.765203156)
2          (555561.692228948, 8561448.0105288)
3        (555666.401386929, 8561893.694598991)
4       (555818.1373583635, 8561625.594414245)
                         ...                  
4547    (554137.1694208445, 8563500.747517455)
4548    (551078.8695353717, 8562426.471542083)
4549    (551215.1880186847, 8562493.736728266)
4550    (552074.8722883032, 8564677.217530191)
4551    (551955.4441563855, 8564454.894890044)
Name: coords, Length: 4552, dtype: object

In [33]:

df_2.plot()
for idx, row in df_2.iterrows():
    plt.annotate(text=row['id'], xy=row['coords'],
                 horizontalalignment='center')

df_2.plot() for idx, row in df_2.iterrows(): plt.annotate(text=row['id'], xy=row['coords'], horizontalalignment='center')

Identificando feição indesejada - médodo 2¶

In [34]:

df_2['AREA_KM2'].max()

df_2['AREA_KM2'].max()

Out[34]:

386.114232

In [35]:

fltr = df_2['AREA_KM2'] == df_2['AREA_KM2'].max()
df_2[fltr].plot()
df_2[fltr].shape, df_2[fltr]["id"].values

fltr = df_2['AREA_KM2'] == df_2['AREA_KM2'].max() df_2[fltr].plot() df_2[fltr].shape, df_2[fltr]["id"].values

Out[35]:

((1, 33), array(['21881'], dtype=object))

Eliminado feição indesejada¶

In [36]:

fltr = df_2["id"] != "21881"
df_2 = df_2[fltr]

fltr = df_2["id"] != "21881" df_2 = df_2[fltr]

Gerando mapa

In [37]:

fig, ax = plt.subplots(1)
df_2.plot(ax = ax, column="NM_SUBDIST" ,cmap="cubehelix", legend=True, legend_kwds={'bbox_to_anchor': (1.5, 1)})

fig, ax = plt.subplots(1) df_2.plot(ax = ax, column="NM_SUBDIST" ,cmap="cubehelix", legend=True, legend_kwds={'bbox_to_anchor': (1.5, 1)})

Out[37]:

<Axes: >

Agregando com dissolve¶

https://geopandas.org/en/stable/docs/user_guide/aggregation_with_dissolve.html

In [38]:

df_3 = df_2[['NM_SUBDIST', "v0001",'v0005', "geometry"]]
df_3 = df_3.dissolve(by='NM_SUBDIST', aggfunc='sum', as_index=False)

df_3.head()

df_3 = df_2[['NM_SUBDIST', "v0001",'v0005', "geometry"]] df_3 = df_3.dissolve(by='NM_SUBDIST', aggfunc='sum', as_index=False) df_3.head()

Out[38]:

	NM_SUBDIST	geometry	v0001	v0005
0	Amaralina	POLYGON ((555768.884 8561020.182, 555765.446 8...	254133.0	1138.743352
1	Brotas	POLYGON ((554383.205 8563533.513, 554358.146 8...	178577.0	900.154859
2	Conceição da Praia	POLYGON ((552582.398 8565306.142, 552579.429 8...	416.0	4.270202
3	Itapoã	POLYGON ((563322.057 8565454.801, 563258.583 8...	191205.0	805.219044
4	Mares	POLYGON ((553634.123 8569419.347, 553605.637 8...	3314.0	37.887135

In [39]:

fig, ax = plt.subplots(1)
df_3.plot(ax = ax,column="NM_SUBDIST", cmap="cubehelix", legend=True, legend_kwds={'bbox_to_anchor': (1.5, 1)})
plt.xticks(rotation=90)

fig, ax = plt.subplots(1) df_3.plot(ax = ax,column="NM_SUBDIST", cmap="cubehelix", legend=True, legend_kwds={'bbox_to_anchor': (1.5, 1)}) plt.xticks(rotation=90)

Out[39]:

(array([535000., 540000., 545000., 550000., 555000., 560000., 565000.,
        570000., 575000., 580000.]),
 [Text(535000.0, 0, '535000'),
  Text(540000.0, 0, '540000'),
  Text(545000.0, 0, '545000'),
  Text(550000.0, 0, '550000'),
  Text(555000.0, 0, '555000'),
  Text(560000.0, 0, '560000'),
  Text(565000.0, 0, '565000'),
  Text(570000.0, 0, '570000'),
  Text(575000.0, 0, '575000'),
  Text(580000.0, 0, '580000')])

Colocando imagens de fundo¶

https://geopandas.org/en/stable/gallery/plotting_basemap_background.html

https://contextily.readthedocs.io/en/latest/

In [59]:

fig, ax = plt.subplots(1, figsize=(10,10))
df_2.plot(ax = ax, column="v0001", cmap='flare',legend=True )
cx.add_basemap(ax=ax, crs=df_2.crs)

fig, ax = plt.subplots(1, figsize=(10,10)) df_2.plot(ax = ax, column="v0001", cmap='flare',legend=True ) cx.add_basemap(ax=ax, crs=df_2.crs)

In [ ]:

# Listas de provedores de mapas online
cx.providers

# Listas de provedores de mapas online cx.providers

In [76]:

fig, ax = plt.subplots(1, figsize=(10,10))
df_2.plot(ax = ax, column="v0001", cmap='flare',legend=True )
cx.add_basemap(ax=ax, source=cx.providers.CartoDB.Voyager ,  crs=df_2.crs)

fig, ax = plt.subplots(1, figsize=(10,10)) df_2.plot(ax = ax, column="v0001", cmap='flare',legend=True ) cx.add_basemap(ax=ax, source=cx.providers.CartoDB.Voyager , crs=df_2.crs)

In [41]:

fig, ax = plt.subplots(1, figsize=(10,10))
df_2.plot(ax = ax, column="v0001", cmap='OrRd',legend=True )
cx.add_basemap(ax, source=cx.providers.CartoDB.PositronNoLabels, zoom=12, crs=df_2.crs)
cx.add_basemap(ax, source=cx.providers.CartoDB.PositronOnlyLabels, zoom=15, crs=df_2.crs)

fig, ax = plt.subplots(1, figsize=(10,10)) df_2.plot(ax = ax, column="v0001", cmap='OrRd',legend=True ) cx.add_basemap(ax, source=cx.providers.CartoDB.PositronNoLabels, zoom=12, crs=df_2.crs) cx.add_basemap(ax, source=cx.providers.CartoDB.PositronOnlyLabels, zoom=15, crs=df_2.crs)

Marcando um ponto por corrdenadas¶

Abra o Google Earth¶

https://earth.google.com/web/

Marque um ponto de localização do seu terreno

In [44]:

from shapely.geometry import Point
# substitua os valores de latitue e longitude pelos valores do seu ponto
# use pontos para separar os decimais
lat = 0
lon = 0
pt_terreno = Point(lon, lat)

from shapely.geometry import Point # substitua os valores de latitue e longitude pelos valores do seu ponto # use pontos para separar os decimais lat = 0 lon = 0 pt_terreno = Point(lon, lat)

aplicando sistema de coordenads

In [45]:

loc_terreno= gp.GeoDataFrame([1], geometry=[pt_terreno], crs="EPSG:4326")

loc_terreno= gp.GeoDataFrame([1], geometry=[pt_terreno], crs="EPSG:4326")

In [46]:

loc_terreno.head()

loc_terreno.head()

Out[46]:

	0	geometry
0	1	POINT (-38.43202 -12.97391)

In [47]:

loc_terreno = loc_terreno.to_crs(df_2.crs)

loc_terreno = loc_terreno.to_crs(df_2.crs)

In [48]:

fig, ax = plt.subplots()

df_2.plot(ax=ax, cmap="Oranges")
loc_terreno.plot(ax=ax)
plt.xticks(rotation=90)

fig, ax = plt.subplots() df_2.plot(ax=ax, cmap="Oranges") loc_terreno.plot(ax=ax) plt.xticks(rotation=90)

Out[48]:

(array([535000., 540000., 545000., 550000., 555000., 560000., 565000.,
        570000., 575000., 580000.]),
 [Text(535000.0, 0, '535000'),
  Text(540000.0, 0, '540000'),
  Text(545000.0, 0, '545000'),
  Text(550000.0, 0, '550000'),
  Text(555000.0, 0, '555000'),
  Text(560000.0, 0, '560000'),
  Text(565000.0, 0, '565000'),
  Text(570000.0, 0, '570000'),
  Text(575000.0, 0, '575000'),
  Text(580000.0, 0, '580000')])

In [49]:

entorno = loc_terreno.buffer(1000)

entorno = loc_terreno.buffer(1000)

In [77]:

fig, ax = plt.subplots()
plt.grid(alpha = .2)
df_2.plot(ax=ax, cmap="Oranges")
entorno.plot(ax=ax)

plt.xticks(rotation=90)
fig.show()

fig, ax = plt.subplots() plt.grid(alpha = .2) df_2.plot(ax=ax, cmap="Oranges") entorno.plot(ax=ax) plt.xticks(rotation=90) fig.show()

Intersects e Overlaps¶

https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoSeries.intersects.html https://geopandas.org/en/stable/docs/reference/api/geopandas.GeoSeries.overlaps.html

In [54]:

fltr = df_2.intersects(entorno[0])
df_4 = df_2[fltr]

fltr = df_2.intersects(entorno[0]) df_4 = df_2[fltr]

In [57]:

fig, ax = plt.subplots()
df_4.plot(ax=ax, cmap="Oranges")
loc_terreno.plot(ax=ax, marker='o', color='teal', markersize=100)
ax.annotate("Terreno", xy= (loc_terreno.geometry.x, loc_terreno.geometry.y), xytext=(3, 3), textcoords="offset points")
plt.xticks(rotation=90)
ax.ticklabel_format(style='plain')
#salvando figura
fig.savefig('./entorno.svg')
fig.show()

fig, ax = plt.subplots() df_4.plot(ax=ax, cmap="Oranges") loc_terreno.plot(ax=ax, marker='o', color='teal', markersize=100) ax.annotate("Terreno", xy= (loc_terreno.geometry.x, loc_terreno.geometry.y), xytext=(3, 3), textcoords="offset points") plt.xticks(rotation=90) ax.ticklabel_format(style='plain') #salvando figura fig.savefig('./entorno.svg') fig.show()